In recent years, the rapid growth of Artificial Intelligence (AI) and the explosion of hardware devices with AI-specific features have led to a rising demand for tools and frameworks capable of translating Deep Learning models from high-level languages like Python into lower-level code optimized for a particular hardware target, often in C. This thesis focuses on edge heterogeneous systems, which have limited computational capabilities, low memory, and prioritize energy efficiency. The proliferation of diverse hardware platforms and programming ecosystems makes porting AI models to every device a non-trivial task. An ideal solution would be a universal tool that can translate high-level model representations, e.g., in Python, into code while accommodating various hardware constraints, programming languages, and interfaces.